The technical field generally relates to contextual modeling of autonomous decision making, and more particularly relates to systems, apparatuses, and methods to embed contextual information into a vector space model to facilitate subsequent lookup, comparison, hypothesizing operations, and associated control of operations of an autonomous vehicle.
As the level of vehicle automation or autonomous functionality continues to increase, the automotive vehicle is more becoming a multi-sensor computational system that is tasked with higher order functionalities such as an understanding of the state of the driver and the state of the driving environment. The system is then tasked with more complex decisions such as requiring processing solutions to be able to identify, understand and adapt to the surroundings during autonomous vehicle operation (e.g. drive slowly when pedestrians are nearby or when driving on poorly maintained roads). To enable the autonomous vehicle to understand and to make complex decisions, there needs to be not only the ability to receive enough information about the surrounding environment but also a processing framework disposed within the vehicle that enables the autonomous vehicle systems to model inputted information and quickly comprehends and processes the information to make the appropriate vehicular control decisions.
The use of machine processing using embedded encodings has been explored in the past primarily with respect to applications in linguistics and other domains, but little, if any, applications of this technology are found or applied in the field of autonomous robotics (i.e. autonomous vehicle systems). The use of such technology has been surmised to have applicability with autonomous vehicle operations in driving scenarios by the use of context embedding vector spaces.
Accordingly, it is desirable to embed contextual information into a vector space model for the following: to facilitate later context matching and action selection, to enable mapping of semantically similar contexts deemed close together, to enable algorithmic solutions for vectors to preserve semantic and syntactic relationships, to forward predict likely contextual scenarios with optimum current control actions, from target contextual objects of systems, apparatuses and methods of an autonomous vehicle. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.